Mô phỏng chấn thương đầu trong tai nạn giao thông đường bộ-Head Injury Simulation in Road Traffic Accidents

Chapter 1Finite Element Head Modelling and Head Injury Predictors 1.1 Head Injury Criteria and Thresholds First, it is important to highlight the terminology used in this book related to

Accidents

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F ábio A O Fernandes • Ricardo J Alves de Sousa Mariusz Ptak

Head Injury Simulation

123

Center for Mechanical Technology and

Automation (TEMA)

University of Aveiro

Aveiro

Portugal

Ricardo J Alves de Sousa

Center for Mechanical Technology and

ISSN 2191-530X ISSN 2191-5318 (electronic)

SpringerBriefs in Applied Sciences and Technology

ISBN 978-3-319-89925-1 ISBN 978-3-319-89926-8 (eBook)

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vii

1 Finite Element Head Modelling and Head Injury Predictors 1

1.1 Head Injury Criteria and Thresholds 1

1.1.1 Injury Criteria Based on Stresses and Strains in the Brain Tissue 4

1.2 Finite Element Head Models 10

References 16

2 Development of a New Finite Element Human Head Model 25

2.1 Introduction 25

2.2 Methods and Materials 27

2.2.1 Geometric Modelling 27

2.2.2 Description of the YEAHM 29

2.2.3 Material Modelling 31

2.2.4 Contact and Boundary Conditions 36

References 36

3 Validation of YEAHM 41

3.1 Simulation of Impacts on Cadavers 41

3.1.1 Intracranial Pressure Response Validation 41

3.1.2 Influence of Mesh Quality on the Results 46

3.1.3 Brain Motion Validation 52

References 57

4 Application of Numerical Methods for Accident Reconstruction and Forensic Analysis 59

4.1 Introduction 59

4.2 Vulnerable Road User Impact—Pedestrian Kinematics 60

4.3 Case Study—Pedestrian Accident Analysis 64

4.3.1 Audi TT Vehicle Measurement 69

4.3.2 Material Testing and Verification 71

ix

4.4 Finite Element Vehicle Model 75

4.5 MultiBody Dummy Model 77

4.6 Vehicle-to-Pedestrian Impact Configuration 78

4.7 Analysis of the Results 81

4.8 Head to Windshield Impact 84

4.8.1 Geometry Acquisition 85

4.8.2 Boundary Conditions 87

4.8.3 Windshield Modeling 89

4.8.4 Analysis of the Results for Head-to-Windshield Impact—Biomechanical Perspective 94

4.9 Conclusions 95

References 96

xi

ROI Region of interest

Chapter 1

Finite Element Head Modelling

and Head Injury Predictors

1.1 Head Injury Criteria and Thresholds

First, it is important to highlight the terminology used in this book related tohead/brain injuries General public more readily associate the negative symptoms

to “brain injury” (judged as more serious) rather than to “head injury” (less serious,

in their view), despite the fact the description may be related to the same injury event(McKinlay2011) The authors of this book will use the terms head/brain injury inter-changeably regarding brain injury Head injury refers to any damage caused to itscontents, for instance a skull fracture or a skin laceration

The cerebral cortex is the largest and most complex part of the brain It consists

of left and right hemispheres, which are interconnected by means of the corpuscallosum These hemispheres are divided into four lobes—frontal, parietal, temporaland occipital (Fig.1.1)

Under the cerebral cortex is the white matter The diencephalon connects thebrain with brainstem, which includes the midbrain, the core and the pons (Andaluz

2016) In the brainstem there are centres that are responsible for the coordination offunctions such as blood circulation, breathing and consciousness (Aare2003) Thecerebellum is in the back of the head and consist of two hemispheres (Andaluz2016)

A common result from traffic accidents are injuries to the middle meningealartery The patient within 30 minutes of injury may not feel any discomfort, yetarterial bleeding leads to detachment of the dura from the cranial vault, resulting

in an epidural hematoma (Aare2003) These and other effects of brain injuries arepresented in Table1.1

Head injury typically results from either a direct impact to the head or from anindirect force applied to the head-neck system, when the torso is rapidly accelerated ordecelerated For both cases, the head sustains a combination of linear and rotationalacceleration (Aare 2003) Generally, translational acceleration creates intracranialpressure gradients, while rotational acceleration rotates the skull relatively to thebrain (Bandak1997a)

For over half a century, research has been undertaken to assess plausible injurymechanisms causing inertial head injury during impact and to establish associatedhuman head tolerance levels The development of injury criteria has been a major goal

© The Author(s) 2018

F A O Fernandes et al., Head Injury Simulation in Road Traffic

Accidents, SpringerBriefs in Applied Sciences and Technology,

https://doi.org/10.1007/978-3-319-89926-8_1

1

Fig 1.1 The brain regions

and their vital functions

(Adapted from Andaluz

2016 and Aare 2003 )

Table 1.1 Relation between symptoms and injured brain regions (Thamburaj2012 )

Frontal lobe Personality; Intelligence; Attention;

Judgment; Body movement; Problem solving; Speech

Loss of movement (paralysis); Repetition of a single thought; Unable to focus on a task; Mood swings,irritability,impulsiveness Changes in social behaviour and personality; Difficulty with problem solving; Aphasia

Temporal lobe Speech; Memory; Hearing;

Sequencing; Organisation

Aphasia; Difficulty recognising faces; Difficulty identifying objects; Problems with memory; Changes in sexual behaviour; Increased aggressive behaviour Occipital lobe Vision Defects in vision or blind spots;

Blurred vision; Hallucinations; Difficulty reading and writing Parietal lobe Sense of touch, pain and temperature;

Distinguishing size, shape and colour; Spatial and visual perception

Difficulty distinguishing left from right; Lack of awareness; Difficulties with eye-hand coordination;

Problems reading and writing; Difficulty with mathematics Cerebellum Balance and coordination Difficulty coordinating and walking;

Tremors; Vertigo; Slurred speech Brainstem Breathing; Heart rate;

Alertness/consciousness

Changes in breathing; Difficulty swallowing; Problems with balance and movement; Vertigo

1.1 Head Injury Criteria and Thresholds 3

among researchers in order to accurately evaluate the risk of sustaining a head injuryand to assess the effectiveness of potential protective head gear such as helmets

In fact, this is still an active area of research and scientists are trying to relate thistype of damage with parameters such as forces or accelerations This may provide astrong basis for improvements in restraint systems design Head injury criteria can

be roughly divided into three categories, as proposed by van den Bosch (2006):

• Injury criteria based on translational or rotational accelerations of the head’s COG,

• Injury criteria based on translational and rotational accelerations of the head’sCOG,

• Injury criteria based on stresses and strains in the brain tissue

Currently, many studies have presented thresholds to assess injury occurrence

A thorough review on head injury predictors and their respective thresholds wasperformed in Fernandes and Alves de Sousa (2015) In this chapter, only injurycriteria based on parameters such as stresses and strains in the brain are addressedsince these are typically used with finite element head models (FEHMs)

The referred types of injury criteria were mainly proposed considering closedhead injury Localised loads, which could be considered suitable criteria for skullfracture, depend on the impactor shape and skull thickness at the impact site Table1.2

presents a summary of fracture peak forces at different regions of the skull.Hume et al (1995) stated that a depressed skull fracture is likely to appear at thetemporal area if the impacted area is less than 5 cm2 and the pressure exceeds 4MPa McElhaney et al (1970), Melvin et al (1970) and Robbins and Wood (1969)

Table 1.2 Peak force for fracture at different regions of the skull

have reported cranial bone stress thresholds According to the mentioned references,

a compact cranial bone breaks in tension at 48–128 MPa, while the cancellous bonebreaks in compression at 32–74 MPa Raul et al (2006) proposed a global strainenergy of 2.2 J as a 50% risk indicator for skull fracture Recently, Monea et al.(2014) suggested an energy failure level of 22–24 J for the frontal site and 5–15 Jfor the temporal region

1.1.1 Injury Criteria Based on Stresses and Strains in the

Brain Tissue

There is a tendency among researchers to use head injury predictors that are based onthe head tissue level response, rather than on its kinematics Brain injury is reported tocorrelate well with stress, strain and strain rate (Lee and Haut1989; Viano and Löv-sund1999) However, strains and strain rates inside the brain are difficult to measure(van den Bosch2006) This can be achieved using anatomical detailed and accurateFEHMs, where stresses and strains are used to compute injury parameters in theskull and in the intracranial contents Therefore, these models bring a detailed injuryassessment closer to reality, since they enable stresses and strains to be examined.DiMasi et al (1995) and Bandak (1995,1997b) developed three component-levelinjury predictors representing the general types of brain injuries: the cumulative straindamage measure (CSDM), the dilatation damage measure (DDM) and the relativemotion damage measure (RMDM) Other predictors have been proposed, such asthe brain pressure tolerance and the brain von Mises stress and also strain

More recently, Takhounts et al (2003, 2008) proposed the SIMon FE modelcriteria based on the above-mentioned injury metrics proposed by DiMasi et al.(1995) and Bandak (1995,1997b) Similarly, other FEHMs have their own specificcriteria and thresholds This is the case of Strasbourg University FEHM (SUFEHM)criteria, which is also reviewed in the this chapter The following subsections coverthe mentioned head injury criteria and their specific thresholds

This is a head injury predictor based on the intracranial pressure Several studieswere published with thresholds for this predictor Some are presented in Table1.3.Liu and Fan (1998), by using a FEHM, concluded that brain pressure has a bettersensitivity for very short time impacts than the head injury criterion (HIC) How-ever, computed brain pressure does not correlate with some brain injuries Kang et al.(1997) and Miller et al (1998) criticised this criterion’s capability to predict braininjuries, particularly diffuse axonal injury (DAI) In addition, Willinger and Baum-gartner (2003b) established that computed brain pressure is not correlated with theoccurrence of brain haemorrhages, whereas brain von Mises stress is

1.1 Head Injury Criteria and Thresholds 5

Table 1.3 Brain pressure thresholds

[kPa]

Reference

Moderate 172.3 Nahum et al ( 1977 )

Severe or fatal 234.4

Minor or absent ≤173 Ward and Chan ( 1980 )

Severe (coup) 235 (Ward et al 1980 and Chafi et al 2009 ) Severe (contrecoup) −186 Ward et al ( 1980 )

Contusions, oedema and

haematoma

200 (Willinger et al 1999b ; Baumgartner 2001 )

and Raul et al ( 2006 )

AIS3+ (coup) 256 Yao et al ( 2008 )

This criterion assumes that the von Mises stress is the cause of brain damage Some

of the proposed thresholds are given in Table1.4

This method was presented by Bandak and Eppinger (1994) to evaluate the related damage within the brain The idea behind their hypothesis is the possibility

strain-to quantify the mechanical damage in the axonal components of the brain, once theresponsible state of strain is characterised

Therefore, a cumulative damage measure based on the brain’s cumulative volumefraction calculation, which has experienced a specific level of stretch (maximumprincipal strain) is used as a possible predictor for deformation-related brain injurysuch as DAI (Marjoux et al.2008; Takhounts et al.2008; Zhang et al.2007).The cumulative strain damage measure (CSDM) is based on the hypothesis thatDAI is associated with the cumulative volume fraction (%) of the brain matter expe-riencing tensile strains over a critical level At each time increment, the volume

of all elements that have experienced a principal strain above prescribed old values is calculated The affected brain volume monotonically increases in timeduring conditions where the brain is undergoing tensile stretching deformations, andremains constant for all other conditions (compression, unloading, etc) Bandak et al.(2001) found that a CSDM level 5 corresponds to mild DAI and a CSDM level of

thresh-22 corresponds to moderate-to-severe DAI, which means that 5% and thresh-22% represent

Table 1.4 Stress thresholds

Brain injury Stress [kPa] Reference

8 (in the temporal lobes) Willinger et al ( 1999b )

MTBI 50% probability: 18 Willinger and Baumgartner ( 2003a , b )

Severe DAI 50% probability: 33

DAI 50% probability: 61.6 Sahoo et al ( 2016 )

respectively the brain volume experiencing strain in excess relative to the criticallevel of 15%, proposed by Thibault et al (1990) Takhounts et al (2003) predicted a50% probability of concussion for 55% of brain volume experiencing a 15% strainlevel Later, Takhounts et al (2008) predicted a 50% probability of DAI for 54% ofbrain volume experiencing a maximum principal strain of 0.25 Recently, as a 50%risk threshold for DAI, Sahoo et al (2016) reported CSDM values of 0.85, 0.59 and0.27 for strains of 0.10, 0.15 and 0.25, respectively

Other proposed values of brain strain critical levels are presented in Table1.5.The CSDM is often considered the most promising stress and strain based injurycriterion, since it is based on the brain’s tissue strain This is an important parameter,mainly when the brain is submitted to considerable rotations that cause large strains,causing brain injuries such as DAI (Aare et al.2003)

1.1 Head Injury Criteria and Thresholds 7

Table 1.5 Strain thresholds

Contusion 50% risk: 0.19 (in the cortex) Shreiber et al ( 1997 )

0.15 (in the cortex) Thibault et al ( 1990 )

0.18 Wright and Ramesh ( 2012 ) 0.2 Morrison III et al ( 2003 ) and

Kleiven ( 2007a ) moderate-to-severe: 0.05-0.10 Margulies and Thibault ( 1992 ) 50% probability of mild: 0.31 Deck and Willinger ( 2008 ) 50% probability of severe: 0.4

Patton et al ( 2013 )

The dilatation damage measure (DDM) is a pressure-based injury criterion proposed

by Bandak (1997b), which evaluates brain injury caused by large dilatational stresses

It is supposed to correlate with contusions (Marjoux et al 2008; Takhounts et al

2008; Zhang et al.2007), by monitoring the cumulative volume fraction of the brainexperiencing specified negative pressure levels

The DDM is similar to the brain pressure criterion presented previously.Nevertheless, this one focuses on the amount of dilatational damage caused by neg-ative pressures, usually associated with contrecoup contusions The probability of

contusion is correlated with the brain volume fraction where negative pressures canproduce damage (Vezin and Verriest2004).

Similarly to the CSDM calculation, at each time step, the volume of all elementsexperiencing a negative pressure level exceeding a prescribed threshold value iscalculated Bandak et al (2001) suggested a DDM value of 5% at a threshold level of

−101 kPa as an injury threshold Takhounts et al (2003) predicted a 50% probability

of contusion for a DDM value of 7.2% for a pressure of−100 kPa

Other researchers have been presenting tolerance values for negative pressures.Ward et al (1980) proposed a value of−186 kPa in tension as a brain tolerance limit.Zhang et al (2004) proposed a value of−76 kPa as a 50% risk of mild traumatic braininjury (MTBI) Yao et al (2006) proposed a critical value for contrecoup pressure of

−130 kPa More recently, Yao et al (2008) presented a critical value for contrecouppressure of−152 kPa as a predictor for AIS3+ injuries

The relative motion damage measure (RMDM) was proposed by Bandak (1997b) toevaluate injuries related to brain movements located at the inner surface of the cra-nium RMDM monitors the brain surface tangential motion resulting from combinedrotational and translational head accelerations Such motions are suspected to be thecause of subdural haematoma (SDH) associated with large-stretch ruptures of thebridging veins (Marjoux et al.2008), due to the brain motion relative to the skull.The bridging veins have been described by Lee and Haut (1989) as having anultimate strain of about 0.5 in tension, while Löwenhielm (1974) observed failure atstrain values ranging from 0.2 to about 1, depending on the strain rate A smaller range

of 0.3–0.6, but still within the range observed by Löwenhielm (1974), was proposed

by Monson et al (2003) and Morrison III et al (2003) Takhounts et al (2003)proposed rupture of the bridging veins for a tolerance limit of 1 More recently,Monea et al (2014) presented a critical value of 5 mm elongation or 25% stretchlimit for the occurrence of acute subdural haematoma (ASDH) due to bridging veinsrupture

The majority of FEHMs do not have bridging veins Nevertheless, RMDM doesnot require the modelling of the bridging veins, but rather the monitoring of the rela-tive displacement between node pairs Each pair represents a bridging vein tetheredbetween the skull and the brain Thus, RMDM relies heavily on the correct modelling

of the interface between brain and skull If the interface is modelled correctly, theRMDM is potentially a suitable injury criterion to predict SDH (Marjoux et al.2008;Takhounts et al.2008)

Numerical head models can be useful tools to reconstruct accidents and even toassess protective head gear In accordance with this line of thought, some research

1.1 Head Injury Criteria and Thresholds 9

groups developed injury specific criteria to their models The simulated injury itor (SIMon), proposed by Takhounts et al (2003), is one of these models It wasoriginally developed by DiMasi et al (1995) and later improved by Bandak et al.(2001) More recently, this model was updated by Takhounts et al (2008), presenting

mon-a new FEHM thmon-at comprised severmon-al pmon-arts: rigid skull, cerebrum, cerebellum, fmon-alx,tentorium, combined pia-arachnoid complex with cerebrospinal fluid (CSF), ventri-cles, brainstem, and parasagittal blood vessels The model’s topology was derivedfrom human computer tomography (CT) The skull was assumed to be rigid, whereasthe rest of the structures were considered as deformable, linear viscoelastic, isotropic,and homogeneous

The SIMon crteria correspond to a set of thresholds obtained through tion of real head impacts These reconstructions were performed by Takhounts et al.(2003, 2008) and the predicted thresholds were already presented in the previoussubsections For instance, a 50% probability of concussion was predicted for:

reconstruc-• a CSDM value of 55% of brain volume experiencing a 15% strain level;

• a DDM value of 7.2% for a pressure of −100 kPa;

In addition, Takhounts et al (2003) proposed rupture of the bridging veins for atolerance limit of 1 More recently, Takhounts et al (2008) predicted a 50% proba-bility of DAI for:

• a CSDM value of 54% of brain volume experiencing a maximum principal strain

of 0.25;

• any brain volume experiencing a maximum principal strain value of 0.87;Similarly to SIMon criteria, SUFEHM has specific thresholds predicted by recon-structing real head impacts with injurious outcomes As described in Willinger andBaumgartner (2003b), three injury criteria are computed with this model:

• The maximum von Mises stress value reached by a significant volume of at least

10 contiguous elements (representing about 3 cm3 of brain volume) is proposed

as a correlation to neurological injury occurrences Marjoux et al (2008), for amoderate and severe neurological injury, obtained von Mises stress values of 27kPa and 39 kPa, respectively More recently, Deck and Willinger (2009) updatedthese tolerance limits to 28 kPa and 53 kPa, respectively;

• The maximum value reached by the global internal strain energy of the elementsmodelling the space between the brain and skull is proposed as a correlation toSDH This value represents the integral ofσ × ε product over the whole domain

between the brain and skull Marjoux et al (2008) found a maximum value reached

by the global strain energy of the subarachnoidal space and proposed it as a lation to SDH with a value of about 4211 mJ This is higher than the 4 J proposed

corre-by COST327 (2001) as strain energy in the CSF, for prediction of SDH Morerecently, Deck and Willinger (2009) updated this tolerance limit to 4950 mJ andproposed a CSF pressure of 290 kPa as tolerance for SDH;

• The maximum value reached by the global internal strain energy of the deformableskull is proposed as a correlation to skull fracture occurrences Marjoux et al

(2008) found an internal energy of 833 mJ A lower value for strain energy nitude (544 mJ) was proposed by Sahoo et al (2013) as threshold for 50% risk

mag-of human skull bone fracture More recently, this value was updated to 448 mJ(Sahoo et al.2014b)

In addition, Deck and Willinger (2008, 2009) proposed a rational approach inorder to evaluate the ability of head models to predict brain pressures and strains byusing a statistical approach, predicting the following thresholds for DAI:

• Brain von Mises stress of 28 kPa for mild DAI and 53 kPa for severe DAI;

• Brain first principal strain of 33% for mild DAI and 67% for severe DAI.All of these predictors are associated with an injury risk of 50% More recently,the von Mises stress was updated to 61.6 kPa and the first principal strain to 0.93for a 50% risk of severe DAI (Sahoo et al.2016) Marjoux et al (2008) assessedand compared the injury prediction capability of the HIC, the Head Impact Power(HIP) and the criteria provided by the SIMon FEHM and SUFEHM Marjoux et al.(2008) found better injury predictions with SUFEHM criteria than SIMon criteria,justifying it with the simplicity of SIMon model, whereas SUFEHM geometry seemscloser to the real head anatomy This was also suggested by Franklyn et al (2003), bycomparing the results obtained with other state-of-the-art FEHM, the Wayne StateUniversity head injury model (WSUHIM), with the SIMon model

Throughout this section, it was evident that there is a wide range of tolerancelevels for each injury criterion that can be justified by different models: physicalhead models, FE models, animal models, clinical and cadaver models (Hrapko et al

2008; Wright and Ramesh2012) Over the years, with the increasing CPU power,FEM appears to be one of the most useful tools for researchers in this field Once

a FEHM is validated and the proper criteria are settled, it may be used to predictaccurately the injury outcome from head impacts During the last decade, complexFEHMs have been developed In the next section, these are reviewed

1.2 Finite Element Head Models

Over the years, FEHMs have been used to understand and predict the head responseunder several impact conditions These models allow an accurate computational-based prediction of brain injuries, by relating the results to medical investigationsbased on autopsies of corpses involved in real accidents (Kang et al.1997) Nowadays,with the huge development of CPU power, head modelling has evolved tremendously.Nowadays, only 3D models are relevant for most impact analysis Nevertheless,2D models are still used for parametric studies of controlled planar motions (Darvishand Crandall2002; Wright and Ramesh2012) Indeed, since a long time ago, there is

a great interest in FE models for head injury research One of the first 3D models wasdeveloped by Ward and Thompson (1975) This is a simple model, with simplifiedgeometries and linear material properties Later, Shugar (1977) developed a 3D

1.2 Finite Element Head Models 11

model, by upgrading a previous 2D version (Shugar and Katona1975) In the sameyear, other simplified models were developed (Khalil and Hubbard 1977; Nahum

et al.1977)

A few years later, a great step was made by Hosey and Liu (1982), presenting ageometric improved FEHM with brain and neck Over the years, more FEHMs hadbeen presented, always with complexer geometries (DiMasi et al.1991; Mendis1992;Ruan et al.1991) In fact, Krabbel and Müller (1996) and Hartmann and Kruggel(1999) developed a FEHM using CT and magnetic resonance imaging (MRI) scans

to model the skull and brain geometries

At this point, some of the current state-of-the-art FEHMs were firstly presented.For instance, the first version of WSUHIM (Ruan et al.1993; Zhou et al.1995,1996).This one was already capable of differentiating the material properties between greyand white matter The second version of WSUHIM was developed and upgraded byAl-Bsharat et al (1999), by introducing a sliding interface between skull and brain.More recently, the final version of WSUHIM (Fig.1.2), was presented by Zhang

et al (2001) This includes scalp, skull, dura, falx cerebri, tentorium, CSF and brainwith distinct white and grey matter Concerning the mechanical properties, the brain

is characterised as viscoelastic and an elastic-plastic material model was used forbone

This model was validated against cadaveric intracranial and ventricular pressuredata (Nahum et al.1977), relative brain/skull displacement data (Hardy et al.2001),and facial impact data (Trosseille et al.1992) It was also validated against pedestrianaccidents data (Dokko et al.2003) In addition, it was used to reconstruct 53 cases

of sport accidents including 22 cases of concussion by King et al (2003)

Another model was developed by Claessens et al (1997), which includes skull,brain and dura mater This model was validated for intracranial pressure, by simu-

Fig 1.2 Wayne State

University brain injury

model (WSUHIM) (adapted

from Zhang et al 2001 )

Fig 1.3 SUFEHM (adapted from Fernandes et al.2013 )

lating the cadaver experiments of Nahum et al (1977) Later, Brands et al (2002)upgraded this model, by incorporating nonlinear material behaviour on the brainresponse Nevertheless, all structures were assumed to be rigidly connected to eachother

Also in the 90s, Kang et al (1997) presented a FEHM that is currently considered

a state-of-the-art model, called SUFEHM The external geometry of the skull wasdigitised from a human adult male and the interior geometry was obtained from anatlas This model also includes other anatomical features such as the scalp, duramatter and brain, as shown in Fig.1.3 Viscoelastic properties were assigned to thebrain and the other features were modelled as isotropic and homogenous (Khaliland Viano1982) This model was validated (Willinger et al.1999a,b,2000c), withregard to cadaveric experiments (Hardy et al.2001; Nahum et al.1977; Trosseille

et al.1992; Yoganandan et al.1994,1995) More details about the development andvalidation of this model are described in Willinger et al (2000a,b), Willinger andBaumgartner (2003a) and Deck and Willinger (2009)

In addition, tolerance limits were identified by Marjoux et al (2008) and Willingerand Baumgartner (2003a) through reconstruction of real accidents, being recognised

as a good DAI predictor (Miller et al.1998; Smith et al.2003) However, a defined correlation between mechanical loading and DAI using FEHM has not beenachieved yet (Cloots et al.2010) A possible contribution to this is that the gyri andsulci in the brain, which are not included in the actual FEHM, can play an importantrole in the local tissue deformations (Cloots et al.2008; Lauret et al.2009) Ho andKleiven (2009) suggested that the inclusion of sulci should be considered in FEHM

well-as it alters the strain and strain distribution

More recently, Sahoo et al (2013,2014b) upgraded SUFEHM, by developing amore realistic skull geometry with a variable thickness, which is able to simulate skullfracture This one was used to reconstruct real-world trauma accidents, developing

a new skull fracture criterion (Sahoo et al.2016b) The brain mechanical propertieswere also improved, focusing on high strain rates and nonlinear behaviour (Nicolle

et al 2004) Later, Sahoo et al (2014) upgraded the model in order to be able tosimulate axonal elongation in cases of head trauma This was validated, showingthe feasibility of integrating axonal direction information into FEHMs This recently

1.2 Finite Element Head Models 13

Fig 1.4 KTH FEHM (adapted from Ho and Kleiven2007 )

upgraded model was used to develop new predictors for DAI, by reconstructing 109head trauma cases (Sahoo et al.2016)

Another state-of-the-art model is the Kungliga Tekniska Högskolan (KTH) humanhead model presented in Fig.1.4 This model was developed by Kleiven (2002)and comprises nonlinear viscoelastic, incompressible material modelling It includesscalp, skull, brain, meninges, CSF and 11 pairs of parasagittal bridging veins Asimplified neck was also modelled

The KTH model has been validated (Kleiven and Hardy2002; Kleiven and vonHolst2001,2002) against experimental pressure data (Nahum et al.1977; Trosseille

et al.1992) and relative motion data (Hardy et al.2001) More recently, it was alsovalidated against intracerebral acceleration experiments (Kleiven2006b) and skullfracture experiments (Kleiven2006a) Kleiven (2007) compared various predictorsfor MTBI, reconstructing real-world accidents

Ho and Kleiven (2007) studied the influence of including vasculature in the KTHmodel by modelling a set of blood vessels and concluded that it could be useful forstudying ASDH, since ruptures can be predicted by measuring the strain directly inthe blood vessels Later, Ho and Kleiven (2009) studied and suggested the inclusion

of sulci in FEHMs, since it alters the strain and stresses distribution in an FE model

In other studies, it is also suggested that the folding structure of the brain surface andthe non-uniform distribution of the CSF greatly influence both the distribution andthe magnitude of the maximum stress and strains in the brain (Cloots et al.2008;Gilchrist and O’Donoghue2000; Lauret et al.2009) The KTH model suffered somemodifications to be used in some specific studies, such as the changes done by Li

et al (2011) in order to model the ventricular system More recently, the influence

of anisotropy was included in this model (Giordano et al.2014), by modelling theneural fibres and thus including the axonal orientation as in SUFEHM (Sahoo et al

2014, 2016)

Another model, the University College Dublin Brain Trauma Model (UCDBTM),based on CT and MRI data, was developed by Horgan and Gilchrist (2003), beingimproved later by Horgan and Gilchrist (2004) The model comprises a scalp, skull,

dura, CSF, falx, tentorium and brain This was validated against intracranial pressuredata from Nahum et al (1977) and brain motion data from Hardy et al (2001).Further validations were accomplished, comparing real-world brain injury events tomodel reconstructions (Doorly and Gilchrist2006) More recently, Yan and Pangestu(2011) improved UCDBTM by including viscoelasticity in the material definition ofalmost all tissues In addition, CSF was modelled as a hydrostatic fluid.

In the last decade, several new models were presented After state-of-the-art els, such as WSUHIM, KTH, SUFHEM and UCDBTM, being developed, the major-ity of these new models did not improve or bring something new Most of them haveoversimplified geometries and material properties, being modelled with linear elasticmodels, with rigid connected parts or were not properly validated (Belingardi et al

mod-2005; Cardamone2005; Dirisala et al.2011; Kim et al.2005; Motherway et al.2009;Suh et al.2005; Ziejewski et al.2009) From this point, only some models are worthmentioning For instance, the SIMon model developed by Takhounts et al (2008)and already presented in Sect.1.1.1.6

Canaple et al (2003) developed a new model, focusing on the representation

of the skull/brain interface and using a hyperelastic material to represent the CSF.Nevertheless, the material properties assigned to the other parts were isotropic andhomogeneous This model was validated for the cadaver impact tests of Nahum et al.(1977) and used in accidents reconstruction (Canaple et al.2002)

A 3D model of the head-neck complex has been developed by Kimpara et al.(2006) including a detailed description of the brain and the spinal cord According tothe authors, the brain-spinal cord model was useful to investigate the central nervoussystem (CNS) injuries This model was validated against three sets of brain test data(Hardy et al.2001; Nahum et al.1977; Trosseille et al.1992) In the same year, Yao

et al (2006) presented a FEHM that includes the main anatomical head structures,such as CSF, meninges and brain This model was validated for Nahum et al (1977)tests, and then used to reconstruct real-world pedestrian accidents (Yao et al.2008;Yang2011)

Iwamoto et al (2002) presented a FEHM that includes a skull, CSF, sagittal sinus,dura, falx cerebri, tentorium and brain with distinct white and grey matter, as shown

in Fig.1.5 This head was developed to incorporate the Total Human Model forSafety (THUMS), a FE model of the entire human body The model was validatedfor head-neck motions, lateral bending and rear end impact (Iwamoto2003) and forexperiments on cadavers (Hardy et al.2001; Nahum et al 1977; Trosseille et al

1992) THUMS was also tested with SUFHEM, showing comparable results (Ipek

et al.2009)

More recently, Mao et al (2013) developed a new FEHM with precise geometriesand validated it for several experimental cases This head model was integratedinto the full body model supported by the Global Human Body Models Consortium(GHBMC) (Schwartz et al.2015) This model is composed by scalp, skull, meninges,bridging veins and brain with distinct white and grey matter Only the meninges weremodelled as linear elastic The others were modelled as viscoelastic or elastic-plasticmaterials This model was validated by Mao et al (2013) for a huge number ofexperimental tests, such as brain pressure (Nahum et al.1977; Trosseille et al.1992),

1.2 Finite Element Head Models 15

Fig 1.5 THUMS model (adopted from Iwamoto et al.2007 )

brain motion (Hardy et al.2001), skull response (Hodgson et al.1970; Yoganandan

et al.1995), among others Nevertheless, significant discrepancies between simulatedand experimental results were observed in a great number of different tests.More information about these and other models can be found in Raul et al (2008)and Tse et al (2014) At the point this work is written, models such as SUFEHM,WSUHIM, KTH, UCDBTM and GHBMC represent the cutting-edge state-of-artfor FEHM All of these use nonlinear material models to simulate brain’s behaviour.Although a great number of FEHMs exist, gyri and sulci are absent in almost allthese models In these, brain’s global geometry is usually similar to a ellipsoidalstructure without sulci and gyri Basically, a simplified volume resembling a brainwith a smooth surface

Cloots et al (2008) reported that gyri and sulci had a significant effect on maximumvon Mises stress value Cloots et al (2010) indicated that a well-defined correlationbetween mechanical loading and DAI using FEHM has not been achieved yet Apossible contribution to this is absence of gyri and sulci in brain models, which canplay an important role in the local tissue deformations (Cloots et al.2008; Lauret et al

2009) The folding structure of the brain surface and the non-uniform distribution ofthe CSF greatly influence both the distribution and the magnitude of the maximumstress and strains in the brain (Cloots et al.2008; Gilchrist and O’Donoghue2000;Lauret et al.2009) In addition, Ho and Kleiven (2009) verified that strain and strainrates during impacts were both reduced in a model with sulci (Ho et al 2009),especially for rotational accelerations in the sagittal plane They also concluded thatthe presence of these structures should be considered in future models

In addition, the relative motion between skull and brain is also important Themajority of these models have different components with shared or rigidly connectednodes, which influence the brain’s intracranial motion Little attention has beenpaid to the relative motion between structures Excessive motion between skull andbrain may injure brain’s surface or even the bridging veins connecting them, whichmay rupture under excessive loading (Horgan and Gilchrist2003; Tse et al.2014).This may cause damage on the brain’s surface (sulci and gyri) and even in the brain

tissue Cerebral contusions usually involve the surface of the brain, especially thecrowns of gyri (Gurdjian et al.1966; Ommaya et al.1971).

After being properly validated, FEHMs can be used as an injury evaluation tool

in accident reconstructions and forensic cases (Tchepel et al.2016b), in testing newmaterials and technologies for safety applications (Fernandes et al.2015, 2017a,b;Ptak et al.2017a) and even in the design and optimisation of personal safety gearsuch as helmets (Fernandes and Alves de Sousa2013; Fernandes et al.2013; Ptak

et al 2017b) In the following chapters, it is described the development of a newFEHM with a brain model with sulci and gyri that also allows the brain to moveinside the skull This model is a new contribution to the state-of-the-art of FEHMs

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Development of a New Finite Element

Human Head Model

2.1 Introduction

Traumatic brain injury (TBI) is one of the main causes of death and disability TBIoccurs when a load exceeds the brain tissue tolerance level (Fernandes and Alves deSousa2015) Road traffic accidents, sports, assaults and work and home accidents arethe major sources In some of these, the evolution of protective head gear is extremelyimportant One way of biomechanically optimising head protective devices is byusing a FEHM

Once properly validated, a FEHM can be used in protective gear design and in thereconstruction of injurious events, by predicting brain injuries under several impactconditions Finite element analysis (FEA) allows to compute variables such as stressand strain, which would be infeasible experimentally (measuring in-vivo) Variablessuch as strain have been pointed out as better injury indicators than externally mea-sured linear or angular acceleration Due to legal and ethical reasons as well as therisk of injury, obtaining data from living human subjects is impossible In the late1970s, Nahum et al (1977) performed impact experiments on cadavers Currently,the results from this publication are still being used as reference in FEHM’s valida-tion

In order to better understand the mechanisms of TBI, several research groups havedeveloped FEHMs, some of them with detailed geometric descriptions of anatomicalfeatures and different material properties (Horgan and Gilchrist2003; Kleiven2007b;Ruan and Prasad1995; Sahoo et al.2014a; Takhounts et al.2008; Yang2011; Zhang

et al.2011) Detailed information about these models and the evolution of FEHMs

is presented in Sect.1.2

The first FEHMs appeared between the late 1970s and early 1980s These weresimple 2D models with some questionable results Since then, the biomechanics ofthe brain for injury analysis and prevention has been a very active area of research(Miller 2011) With the increasing CPU power, more complex models have beendeveloped

More realistic 3D models were only possible in the 90s and further with theadvances in computing (Horgan and Gilchrist2003; Kleiven2007b; Mao et al.2013;Sahoo et al.2014a; Takhounts et al.2008; Yang2011; Zhang et al.2001) These are

© The Author(s) 2018

F A O Fernandes et al., Head Injury Simulation in Road Traffic

Accidents, SpringerBriefs in Applied Sciences and Technology,

https://doi.org/10.1007/978-3-319-89926-8_2

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26 2 Development of a New Finite Element Human Head Model

the more complex ones found in the literature There are a great number of othermodels, but these are oversimplified or not validated

Although a great number of FEHMs exist, gyri and sulci are absent in almostall these models In these, brain’s global geometry is usually similar to spheroidal/ellipsoidal structures, without sulci and gyri Basically, a simplified volume resem-bling a brain with a smooth surface Cloots et al (2008), using a 2D FE model,reported that gyri and sulci had a significant effect on von Mises stress maximumvalue Additionally, Cloots et al (2010) indicated that a well-defined correlationbetween mechanical loading and DAI using FEHM has not been achieved yet Apossible contribution to this is absence of gyri and sulci in brain models, which canplay an important role in the local tissue deformations (Cloots et al.2008; Lauret et al

2009) The folding structure of the brain surface and the non-uniform distribution

of the CSF greatly influence both the distribution and the magnitude of the mum stress and strains in the brain (Cloots et al.2008; Gilchrist and O’Donoghue

maxi-2000; Lauret et al.2009) In addition, Ho and Kleiven (2009) verified that strain andstrain rates during impacts were both reduced in a model with sulci, especially forrotational accelerations in the sagittal plane They also concluded that the presence

of these structures should be considered in future models Figure2.1shows in detailsulci and gyri structures

Gyrus

Sulcus

Fig 2.1 Illustration of the structures gyri and sulci

The relative motion between skull and brain is also important to model Themajority of these models have shared or rigidly connected nodes, which influencethe brain’s intracranial motion Little attention has been paid to the relative motionbetween structures Supported on this, Claessens et al (1997) created a geometricallysimple FEHM where structures inside the head have the ability to move relative toone another.

Excessive relative motion between skull and brain may injure brain’s surface oreven the bridging veins connecting them, which may rupture under excessive loading(Horgan and Gilchrist2003; Tse et al.2014) In addition, excessive relative motionmay cause damage on the brain’s surface (sulci and gyri) and even inside the brain.Cerebral contusions usually involve the surface of the brain, especially the crowns

of gyri (Gurdjian et al.1966; Ommaya et al.1971)

Thus, in this chapter, it is presented the modelling and validation of a FEHMwith a brain model with sulci and gyri This model will also allow the brain to moveinside the skull The model developed and validated in this work can give a greatcontribution in predicting brain injuries, using also proper criteria For instance,cerebral contusions due to the geometrical detail of the brain surface

2.2 Methods and Materials

In this work, a FEHM is developed Different steps were necessary to model it:geometric modelling, material modelling, contact definition and validation In order

to validate it, the experiments performed by Nahum et al (1977) and Hardy et al.(2001) in cadavers were simulated These are important to validate the brain response

in terms of pressure and motion, respectively

2.2.1 Geometric Modelling

In this work, the head modelled is based on medical images These are typicallyused to correctly model the human body Computer tomography (CT) and magneticresonance imaging (MRI) are usually used to observe what is happening inside ourbodies The first technique, CT, is normally employed to observe bone structures,whereas MRI technique is suitable for soft tissues Thus, in this work, CT and MRIwere used to generate the skull’s and brain’s geometry, respectively

In order to accurately generate skull’s geometry, 460 images spaced 1.5 mm andobtained from CT scans were used From this set of slices, the skull’s geometrywas extracted by creating a region of interest (ROI) with the Osirix software (Osirix

2003) This skull’s ROI was created by automatic segmentation using Osirix’s

plug-in, MIA Afterwards, this ROI was manually adjusted in some slices at the sagittaland coronal planes in order to improve the skull’s geometry, as shown in Fig.2.2

28 2 Development of a New Finite Element Human Head Model

Fig 2.2 CT scans used to model the skull geometry

Brain’s geometry was generated from segmentation of MRI data, employing thesame technique used for the skull The MRI data consisted of 181 T2-weighted slicestaken at 1 mm intervals in a human male head With T2 weighted images, it waspossible to distinguish the brain from the other intracranial contents Some manualadjustments were applied at all the three planes, sagittal, coronal and axial, in order toimprove the skull’s geometry Nevertheless, after manual segmentation and geometrygeneration, some irregularities and deviations were still present These were lightlysmoothed using Meshmixer software (Meshmixer2012), without compromising themodel’s global geometry In addition, a software named Meshlab was also used

in order to close any existing gaps in the triangular mesh of the geometric model(STereoLithography (STL) model) (Cignoni et al.2008) Both STL meshes have asuitable amount of triangles, generating precise geometries without overloading thecomputer

These STL models were then imported to CATIA V5 in order to create 3D solidcomputer-aided design (CAD) models (CATIA V5 2008) After successfully gen-erating skull and brain CAD models, the space between skull and brain was used tomodel the CSF In other words, brain and skull models acted as “sculpting moulds” inthe modelling of CSF, as shown in Fig.2.3 Finally, these CAD models were importedinto Abaqus, creating the FE meshes Figure2.3shows a summary of the methodol-ogy used to create the geometry of the YEt Another Head Model (YEAHM)

Medical Images STL model

CAD model

FE model

Fig 2.3 Methodology used to model YEAHM geometry

2.2.2 Description of the YEAHM

The human brain can be simply described as a soft highly metabolically active tissue,floating in CSF within the rigid cranium (Bilston2011) These protect the brain fromexternal mechanical loads experienced by the head during normal daily life Thus,YEAHM consists of skull, CSF and brain as shown in Fig.2.4 This shows a crosssection of the model and illustrates the anatomical features of the head

The brain model has all important sections: frontal, parietal, temporal, and tal lobes, both hemispheres, cerebrum, cerebellum, corpus callosum, thalamus, mid-brain, and brain stem It was not possible to separately segment CSF and structuressuch as membranes and bridging veins because of the resolution of MRI data Then, avolume was created to represent all these parts between skull and brain It was named

occipi-as CSF due to its larger volume Also, there is no consensus if cerebral voccipi-asculatureshould be included or not in head modelling (Ho and Kleiven2007; Zhang et al

2002)

In addition, the cerebral ventricular system was also modelled and filled with CSF.The CSF is described using solid elements with a low shear modulus, as in otherpublications (Yang2011) The global CSF model is a combination of the CSF andthe meninges For instance, the inner surface of the CSF model acts as the pia mater,surrounding the brain and dipping down into sulci and fissures and thus, acquiringthe brain shape

30 2 Development of a New Finite Element Human Head Model

YEt Another Head Model (YEAHM)

CSF

Fig 2.4 YEAHM consists of skull (blue), CSF (red) and brain (green)

The adult human skull is made up of eight bones that are rigidly connected bysutures For this reason, there is no need to model them as separate bones It hasbeen reported that the skull thickness can vary from 4 to 9 mm (Kleiven2002; Ruanand Prasad 2001) YEAHM’s skull has a variable thickness in this range, beinggeometrically accurate In addition, most FEHMs developed so far have a skull withuniform thickness (Yang and King2011)

In addition to the ventricles and the skull with a variable thickness, the latter wasalso modelled with some of its real irregularities at the base Ivarsson et al (2002)indicated that the ventricles and the irregular skull base are necessary in modellinghead impact, since the latter protects nerves and vessels passing through the cranialfloor by reducing brain displacement Ivarsson et al (2002) also concluded that CSFrelieves strain in regions inferior and superior to the ventricles This is supported byKleiven (2005), observing also low levels of strain in the vicinity of the ventricles,probably due to strain relief around them

All parts were modelled as solid Due to the complex geometry of skull, brain andCSF, these were meshed with tetrahedral elements The YEAHM is constituted by

a total of 991617 second order ten-node tetrahedrons More details about the meshare presented in Table2.1

Table 2.1 YEAHM’s mesh info